Tracking a changing environment: optimal sampling, adaptive memory and overnight effects

Abstract

Foraging in a variable environment presents a classic problem of decision making with incomplete information. Animals must track the changing environment, remember the best options and make choices accordingly. While several experimental studies have explored the idea that sampling behavior reflects the amount of environmental change, we take the next logical step in asking how change influences memory. We explore the hypothesis that memory length should be tied to the ecological relevance and the value of the information learned, and that environmental change is a key determinant of the value of memory. We use a dynamic programming model to confirm our predictions and then test memory length in a factorial experiment. In our experimental situation we manipulate rates of change in a simple foraging task for blue jays over a 36 hour period. After jays experienced an experimentally determined change regime, we tested them at a range of retention intervals, from 1 to 72 hours. Manipulated rates of change influenced learning and sampling rates: subjects sampled more and learned more quickly in the high change condition. Tests of retention revealed significant interactions between retention interval and the experienced rate of change. We observed a striking and surprising difference between the high and low change treatments at the 24 hour retention interval. In agreement with earlier work we find that a circadian retention interval is special, but we find that the extent of this ‘specialness’ depends on the subject’s prior experience of environmental change. Specifically, experienced rates of change seem to influence how subjects balance recent information against past experience in a way that interacts with the passage of time.

Comparison of plant preference hierarchies of male and female moths and the impact of larval rearing hosts

“…It provides a better monitoring of environmental cues and tuning to current conditions, allowing a better match between expressed phenotype and the environmental conditions (Auld et al 2010, Dunlap andStephens 2012). Preference hierarchies of males and females may converge to a great extent, in spite of the great differences manifested in their respective reproductive behaviors (mate finding vs. oviposition), through a combination of innate preferences and previous experience.…”

“…Plasticity in host plant choice is an important property for an herbivorous insect to adapt to changing environments (Agrawal 2001, Matsubayashi et al 2010, Mery and Bruns 2010, Dunlap and Stephens 2012. Using earlier experience is one way to cope with variable and unpredictable environments (Dukas 2008, Dunlap andStephens 2012). However, for the experience to be beneficial, it is important that the environment is stable enough for the acquired information to be used in a reliable way (Leimar et al 2006).…”

The effects of the previous outcome on probabilistic choice in rats.

“…Although previous studies have analyzed the effect of transitions of the probability of food delivery on subsequent choices (e.g., Mazur, 1995), the current dynamic probability training, to our knowledge, has not been employed previously, nor have there been any direct comparisons of static and dynamic probability adjustments like those used in the present experiment (but see Dunlap & Stephens, 2012, for related work). The typical adjusting procedures described here have involved an adjustment of the amount of or the delay until reward, and the corresponding analyses are commonly derived from a molar perspective (but see Cardinal et al, 2002).…”

The Organization of Behavior over Time: Insights from Mid-session Reversal

“…With MSR tasks, subjective differences in reinforcement probability might also occur at the endpoints of the session when the birds are good at each task. As these probabilities subjectively converge in the more challenging middle part of the session, pigeons might also begin dynamically exploring or sampling the two alternatives to a greater degree (Dunlap & Stephens, 2012; Lea, McLaren, Dow, & Graft, 2012). In the future, it will be interesting to see if the configurations of other MSR tasks result in the same properties of short and long run locations.…”